Inequalities within women’s healthcare do not only impact conditions solely affecting women, but also our understanding of the different physiological responses that women have in areas such as cardiac disease and immune responses. It is also known that women use health technology differently to men, presenting clear opportunities to deliver measurable health benefits to more than half of the population, as well as a huge market opportunity for businesses to target.<\/p>\n
Femtech and women\u2019s health innovation are increasingly growing areas, however there is a risk that the promise of these areas is not realised without recognising the challenges that remain. Dr MaryAnn Ferreux, Medical Director at Health Innovation Kent Surrey Sussex (HIKSS), and Melissa Ream, Specialist Commercial Advisor, HIKSS, share their perspectives on the potential opportunities in femtech and women\u2019s health, and how we can work together to reap the benefits.<\/p>\n
MaryAnn Ferreux:<\/strong> The main challenges in women\u2019s health stem from decades, if not centuries, of gender bias and discrimination. In the past, many women\u2019s health complaints were attributed to being emotional or hysterical and these gender stereotypes often led to doctors mistreating women\u2019s symptoms as a mental health condition, rather than a physical condition.<\/p>\n While that has changed, much of this inherent gender bias remains, with many clinical trials and research studies not assessing the impact on women<\/a>. We have recognised that there is a gender-based data gap but now we need to overcome this. Data sets are very rarely analysed by gender, and yet it is almost universally recorded, so the disparities in how genders respond in different disease groups could and should be analysed routinely.<\/p>\n Melissa Ream:<\/strong> We often think about women\u2019s health in terms of women\u2019s conditions, be that menstrual health, maternity or menopause. But women\u2019s health care is general health care too. The cardiac symptoms and risks for women are different to men, yet these are not widely known. And this comes down to under representation of women in data sets as well as unconscious bias in the wider world. If you search for images of people having a heart attack on Google, most of the images will be of men. Cardiovascular disease in women is a bigger killer than breast cancer<\/a> and we need to start taking this more seriously, looking at how clinical services are designed, delivered and promoted.<\/p>\n MaryAnn Ferreux:<\/strong> AI has a lot of potential to improve the health experiences of women, but there is a risk of building in more inequality if we do not address gender bias in data sets. More and more innovators are wanting to use AI in their technologies but some of them are not thinking about bias until it\u2019s too late. A global analysis of AI systems found that 44 per cent demonstrated a gender bias<\/a>. We need to ensure that the data sets used are comprehensively analysed and shown to be relevant to the target population and this comes down to the decision makers asking the right questions, whether that\u2019s innovators, regulators, funders or purchasers. I\u2019m also concerned about a lack of leadership in AI regulation and who is at the decision-making table. Without diversity at that top level, it is unlikely that the right questions will be asked early enough \u2013 retrofitting later on just won\u2019t work!<\/p>\n Learning from experience presents a huge opportunity, but one that we haven\u2019t been previously good at. As an example, a lack of ethnicity data incorporated into skin algorithms resulted in racial bias in pulse oximetry<\/a>, ensuring that the device was not as effective for black and ethnic minority people. This disparity has been observed since the 90s<\/a> and yet the device was still used during the Covid-19 pandemic, resulting in worse outcomes for black and ethnic minority people. We didn\u2019t address the problem when we had the chance, and we need to ensure this doesn\u2019t happen again.<\/p>\nDo you think AI has the power to change this or do these concerns remain?<\/h3>\n
So, what do we need to do to support equality in healthcare?<\/h3>\n